{"id":25063101,"url":"https://github.com/dahmansphi/analysis_from_start_to_end","last_synced_at":"2026-01-08T15:38:56.783Z","repository":{"id":226898487,"uuid":"769914383","full_name":"dahmansphi/analysis_from_start_to_end","owner":"dahmansphi","description":"The Big Bang of Data Science- Analysis from the Start to The End- [Book Two]","archived":false,"fork":false,"pushed_at":"2024-03-15T07:35:37.000Z","size":5075,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-06T17:47:32.719Z","etag":null,"topics":["analysis","data","data-analytics","data-mining","data-science","hypothesis-testing","jamovi","machine-learning"],"latest_commit_sha":null,"homepage":"https://www.dahmansphi.com/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dahmansphi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-03-10T12:25:53.000Z","updated_at":"2024-03-10T12:28:52.000Z","dependencies_parsed_at":"2024-03-15T08:47:09.043Z","dependency_job_id":null,"html_url":"https://github.com/dahmansphi/analysis_from_start_to_end","commit_stats":null,"previous_names":["dahmansphi/analysis_from_start_to_end"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahmansphi%2Fanalysis_from_start_to_end","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahmansphi%2Fanalysis_from_start_to_end/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahmansphi%2Fanalysis_from_start_to_end/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dahmansphi%2Fanalysis_from_start_to_end/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dahmansphi","download_url":"https://codeload.github.com/dahmansphi/analysis_from_start_to_end/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246465224,"owners_count":20781919,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","data","data-analytics","data-mining","data-science","hypothesis-testing","jamovi","machine-learning"],"created_at":"2025-02-06T17:41:12.785Z","updated_at":"2026-01-08T15:38:56.741Z","avatar_url":"https://github.com/dahmansphi.png","language":null,"funding_links":["https://patreon.com/user?u=118924481"],"categories":[],"sub_categories":[],"readme":"\u003e [!IMPORTANT] \n\u003e make sure to check the book [competitive advantages](#book-competitive-advantage) below. \n\n![the big bang of data science banner.](/assets/cover_page.jpg)\n\n\u003e [!NOTE]\n\u003e 1. To view the *project video introduction* please visit [Main Introduction to The Big Bang of Data Science- First Edition](https://youtu.be/0weCBnNO7tk).\n\u003e 2. To view the *project documentation* please visit [The Big Bang of Data Science Project](https://github.com/dahmansphi/big_bang_of_data_science_project)\n\n\n# About the Second Book- Analysis From the Start to the End\n\n\u003e [!IMPORTANT]\n\u003e 1. you can have an author introduction to this book on [Welcome, by the author, to the Second Book- Analysis from the Start to The End](https://youtu.be/dR8PQBVfgHI) \n\u003e 2. You can have a video presentation to this book on [Analysis from The Start to The End- Chapters Review](https://youtu.be/ctGVq0aO4hg)\n\u003e 3. you can have a video screencast on the all the chapters' contents on [Screencasts from Analysis from the Start to The End Chapters- Book Two](https://youtu.be/6vH_G2zn6Gg)\n\n\n## Author's Words\nWelcome to **Analysis From the Start to the End** official documentation, the **second book** from [The Big Bang of Datat Science](https://github.com/dahmansphi/big_bang_of_data_science_project). I am Dr. Deniz Dahman the creator of the [BireyselValue algorithm](https://github.com/dahmansphi/bireyselvalue_v1/tree/main) and the author of this digital book. In the following section you will have a brief introduction on the main contents of this book.  \nIn addition, a reference to available [outlets](#outlets) where you may have access to the entire recorded lessons. Before going ahead, I would like to let you know that I have done this project as an independent scientist without any fund or similar capacity. I am dedicated to proceeding and seek further improvement of the content of this material. To this end if you wish to contribute in any way to this work, please find further details in the contributing section.  \n  \n## Contributing \n\nIf you wish to contribute to the creator of this method and the author, you may want to check possible ways on: \n\n\u003e `To Contribute in any way possible, thank you, you can check` :\n\n1. view options to subscribe on [Dahman's Phi Services Website](https://dahmansphi.com/subscriptions/)\n2. subscribe to this channel [Dahman's Phi Services](https://www.youtube.com/@dahmansphi)     \n3. you can support on [patreon](https://patreon.com/user?u=118924481) \n\n\nIf you prefer *any other way of contribution*, please feel free to contact me directly on [contact](https://dahmansphi.com/contact/). \n\n*Thank you*\n\n\n# Book TWO- Analysis From the start to the end\n\n## Book Cover\n![the analysis cover book.](/assets/titles_analysis_s2e.png)\n\u003e [!TIP]\n\u003e You can have full access to the material of this book from the [outlet](#outlets) section below. Thank you.\n\n## The Content\n\nThis is the second element of **the Big Bang of Data Science**, **`Analysis from the Start to The End`**.  \nI don’t want to stick to that _abstract and direct_ definition from the academic book, on the meaning of analysis, but from the industrial one. So, I believe **ANALYSIS** is the _co-concertmaster_ that sits in the second chair of the highest leadership position among all the other parts that are responsible for the outcome of a product. \nAnalysis is an art that has the characteristics of being a two-edged sword. In other words, if your understanding of analysis is based on **subjective, rigid ground** then your answers/solutions/products are for sure **questioned**. However, if your analysis is based on **objective, scientific grounds** then your answers/solutions/products are for sure **worthy of consuming**. If you search any search engine the word of analysis, you should not be surprised with the astronomical number of results on your search. The problem with many of the materials which discuss the subject of analysis is that **two perspectives are there**: \n\n\u003e the first, the perspective of analysis as a bunch of graphs and tables, \n\n\u003e and the second, the perspective of analysis is a bunch of tests and tools that applies them. \n\nWell, one can argue there is nothing wrong with that, but the problem arises when one fails to understand the raw materials that are needed to present those tables and figures, in addition, the fundamentals of those tools and tests that produce them. To this end, **this book aims to address this mis conceptual understanding about analysis**; basically, the book materials are constructed in such way that one can:\n \n\u003e firstly, understand the important of data that come from solid research, \n\n\u003e secondly, to understand the fundamentals of analysis from philosophical and scientifical perspective, \n\n\u003e thirdly, complete grasp on the meaning of hypothesis, as forming, articulating, etc., \n\n\u003e and finally, the comprehensive knowledge on the tests and tools are there to help you implement your analysis. \n\nTo this end, **the second book** is carefully crafted to meet all the requirements to build your product on the right foundation of analysis. Here is a quick view of the content of the book. \n\n### Introduction \n\n1. [✓] Research map   \n\n2. [✓] THREATS TO CONCLUSION VALIDITY  \n\n3. [✓] STATISTICAL POWER  \n\n4. [✓] IMPOROVE CONCLUSION VALIDITY \n\n5. [✓] ANALYSIS \n\n### Data Preparation \n\n1. [✓] LOGGING THE DATA  \n\n2. [✓] DATA ACCURACY CONTROL  \n\n3. [✓] DATABASE STRUCTURE  \n\n4. [✓] ENTERING DATA TO THE COMPUTER \n\n5. [✓] DATA TRANSFORMATION \n\n6. [✓] LAB-01- Three parts on data preparation \n\n### Descriptive Statistics \n\n1. [✓] Introduction to EDA  \n\n2. [✓] Distribution  \n\n3. [✓] Central Tendency \n\n4. [✓] Dispersion\n\n5. [✓] Bivariate descriptive\n\n6. [✓] Multivariate descriptive\n\n7. [✓] LAB-02- analysis on univariate, bivariate, and multivariate\n\n\n### Inferential Statistics  \n\n1. [✓] Introduction  \n\n2. [✓] Esstimating Parameters  \n\n3. [✓] Hypothesis Testing  \n\n\n### Statistical Software \n\n1. [✓] Introduction  \n\n2. [✓] Statistical Software\n\n3. [✓] Intro- Implementation by *JAMOVI*\n\n4. [✓] LAB-03- analysis on two datasets using *JAMOVI*\n\n\n### LAB-Section –04- analysis on real dataset using jamovi \n\n1. [✓] Review  \n\n2. [✓] EDA analysis  \n\n3. [✓] Inferential Analysis  \n\n4. [✓] LAB-04- implementation on the dataset from the first book\n\n\n## Who is this book for?\n\nThis book is for anyone, regardless of the educational background, with the interest in building, creating and producing a professional product that has a vision of the future. You don’t have to have specific skill in any way, but extreme enthusiasm to learn how to make the right decision. So, it is meant for an audience of: (1) students, under or postgraduate. (2) scholars, (3) researchers, (4) scientists, (5) executives, (6) managers, (7) professionals, (8) or laypersons. \n\n\u003e [!TIP]\n\u003e The trainer strongly advice on learning the materials from the first book [Research from the Start to the End](https://github.com/dahmansphi/research_from_start_to_end); that can absolutely help you to perform way better in this book.\n\n# Book competitive advantage\n\n\u003e [!IMPORTANT] \n\u003e 1. In this book you will find **a few outlines** that deliver competitive advantages.  \nas outlined above in the introduction, this book is **the second book** from **The Big Bang of Data Science** that means it’s an element among other elements of a project. This implies that the outlines and the contents are not ONLY discussed from an analysis perspective, but also from a wider perspective of the entire project. This offers you an opportunity to excel in the subject of analysis from a wider range of disciplines.  . \n\u003e 2. As I have outlined above in the introduction, so many materials discuss the subject of analysis, however, many of which fail to focus on the subject of **orientation**. If your analysis is **subjective oriented**, i.e., your analysis is controlled by external factors such as your background, education, environment, culture and many more, then your final solution is questionable. However, if your analysis is **objective oriented**, i.e., your analysis is based on methodical, and scientific facts, then your final product is worthy of consuming. This material is constructed based on the latter, that is **objective oriented** approach.  \n\u003e 3. The slogan of the **Big Bang of Data Science** is **From academia to industry**, this material is obligated to that. You will have two types of labs: the first is using synthetical type of data to implement the abstracts and theories you learn, and the second uses a real dataset that we have built from **the first book Research from the Start to the End**. As a result, you will master the idea from abstract to applied.\n\u003e 4. Lastly, all the types of tests we are going to learn about will be executed using an open-source statistical tool, that is **Jamovi**. This tool offers several statistical tests that one needs to do research analysis. Notably, unlike other material that presents **analysis** within the framework of jamovi, this material coaches you how to understand the selection of the right test, first, then you can use this tool or any other tool of your choice to execute the test. So, this perceptive gives you confidence in relying on many other tools of your choice if you understand each test independently.    \n\n# Outlets\n\nyou can have access to the recorded lessons of this book from these outlets:\n1. [outlet_1 + Discount](https://dahmansphi.com/analysis-s2e/). \n2. [outlet_2](https://www.udemy.com/course/analysis-s2e/?referralCode=D6AE65044A056A93826E)\n\n# The digital copy \nyou can have access to the ppt digital copy in pdf format from [digital ppt book](/assets/analysis_s2e_final.pdf) \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdahmansphi%2Fanalysis_from_start_to_end","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdahmansphi%2Fanalysis_from_start_to_end","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdahmansphi%2Fanalysis_from_start_to_end/lists"}